Fannie Mae mortgage data

Import cuXfilter

[1]:

from cuXfilter import charts
import cuXfilter
from bokeh import palettes
from cuXfilter.layouts import *


from bokeh.tile_providers import get_provider as gp
tile_provider = gp('STAMEN_TONER')

Define charts

[3]:

cux_df = cuXfilter.DataFrame.from_arrow('../../../data/146M_predictions_v2.arrow')

[4]:

chart0 = charts.bokeh.choropleth(x='zip', y='delinquency_12_prediction', aggregate_fn='mean', geo_color_palette=palettes.Purples9,
                                  geoJSONSource = 'https://raw.githubusercontent.com/rapidsai/cuxfilter/master/javascript/demos/GTC%20demo/src/data/zip3-ms-rhs-lessprops.json',
                                  tile_provider=tile_provider, data_points=1000)

chart2 = charts.bokeh.bar('delinquency_12_prediction',data_points=50)
chart3 = charts.panel_widgets.range_slider('borrower_credit_score',data_points=50)

Add mappings for chart1

[5]:

mapper1 = {}

for val in cux_df.data.dti.unique().to_pandas().tolist():
    mapper1[int(val)] = 'l_'+str(val)

chart1 = charts.panel_widgets.drop_down('dti', label_map=mapper1)
#also available: panel_widgets.multi_select

Create a dashboard object

[6]:

d = cux_df.dashboard([chart0, chart1], layout=feature_and_base, theme=cuXfilter.themes.dark, title="Fannie Mae mortgage Dashboard")

[7]:

#add more charts
d.add_charts([chart2, chart3])

[8]:

#dashboard object
d

[8]:

Starting the dashboard

  1. d.show(‘url you want the dashboard to run’) remote dashboard

  2. d.app(‘10.110.47.43:8888’) within the notebook: If you are using jupyter remotely, use this line instead of second, and replace url with current notebook url

[9]:

# preview the dashboard
await d.preview()
../_images/examples_Mortgage_example_14_2.png

Export the queried data into a dataframe

[10]:

queried_df = d.export()

no querying done, returning original dataframe